Provision of snow water equivalent from satellite data and the hydrological model PROMET using data assimilation techniques

Abstract

Information on snow cover and snow properties is an important factor for hydrology and runoff modelling. Frequent updates of snow cover information can help to improve water balance and discharge calculations. Within the frame of polar view, snow products from multisensoral satellite data are operationally provided to control and update water balance models for large parts of Southern Germany. Optical AVHRR sensors of the NOAA satellite are used for snow mapping and snow line delineation. Although these acquisitions are available several times per day, cloud cover hinders frequent updates of snow cover maps. As an additional remote sensing data source microwave data from ASAR on ENVISAT is used. Since C-band SAR sensors are only sensitive to snow with a high content of liquid water, the application of ASAR is limited to the melting periods. However under these conditions the developed procedure allows not only to delineate the snow cover in a comparable way as from optical data, also the additional information where the snow is melting is provided. In order to demonstrate how the remote sensing products can be used for improved water balance modelling, an application example for the watershed of the Upper Danube will be presented. This testsite is the research area of the integrative research project GLOWA-DANUBE that is conducted by the University of Munich. Model results using the PROMET-model of snow distributions with and without data assimilation of the remote sensing products will be given. Developed data assimilation concepts will be presented. Through data assimilation, the modelled snow cover agrees better with the mapped snow cover information from satellite. The optimised model provides maps of snow water equivalent, that can not directed be assessed by remote sensing. The impact of data assimilation on the modelled runoff will thus further be analysed.